Yoon-Ki Kim, Doo-Hyun Hwang and Chang-Sung Jeong, Korea University, South Korea

Face detection algorithms are used to detect the human in various industry fields. A typical face detection algorithm such as Haar Feature-based Cascade Classifier gives us an easier way to detect human face. It consists of several classifiers which contain complicated arithmetic operations. Several classifiers constitute the cascade which can detect each element of human face. The more cascades are contained in the algorithm to detect elements of human face, the more it takes a time to detect human face. The previous cascade hardly recognize real human, since previous cascade processes only one source from image source. In this paper, we present a new cascade method for human face detection which exploits several classifiers for data not only from image source but also various heterogeneous sensors. Cascades consist of various sensors based on tuple data type could be operated quickly. It provides more accuracy of real human face detection, reduces the number of classifier for high speed processing in real-time.

Face Detection, Heterogeneous Sensor, Real-Time Processing, Haar-Like Feature